ABSTRACT Title: Mammographic Density and Metabolic Genotyping for Predicting Cancer Prognosis This project will investigate the role of quantitative mammographic density (MD) and cytochrome P450 CYP2D6 metabolic genotyping in the prognosis of breast cancer (BC) patients, with the ultimate goal of using them as prognostic predictors for improving the treatment that can be provided to each individual patient. Mammographic density is an established risk factor for developing breast cancer, and there is also evidence suggesting that cancers arising from dense tissue area are more aggressive; therefore collectively they suggest MD may serve as a prognostic predictor. In this project we will utilize a single-institution, all- Chinese, patient cohort that is treated in one hospital using similar strategies (Taichung Veteran's General Hospital in Taiwan). Their Breast Care Center has established a detailed registry, and each patient's personal factors, TNM staging, molecular biomarkers, imaging findings, and treatment protocols (surgery, radiation, chemotherapy and hormonal therapy) are all well documented in the database. Patients with newly diagnosed Stage I, II, and III invasive breast cancer will be identified from the registry as the inclusion criteria. The majority of patients are continuingly being followed in the same hospital, so it is very easy to find their prognostic information, including development of recurrence, secondary BC, distant metastasis and BC-specific death. This registry provides a great resource for investigating the association of MD with patients' prognosis (Aim-1). For patients diagnosed with hormonal receptor positive BC, it is the standard of care to give them hormonal therapy, e.g. tamoxifen for pre- and peri-menopausal women. Although the treatment has been shown very effective in improving disease-free survival and overall survival on a statistical basis, many patients still develop progressive disease, raising the question of individual responsiveness. The hormonal therapy drugs are associated with various side effects; thus there is a strong interest to find biomarkers that can predict the responsiveness of each individual patient to ensure a favorable benefit-to-risk ratio. MD reduction has been shown as a valid surrogate marker for predicating tamoxifen response, and it would be very interesting to understand why some women would respond and show MD reduction but others not. Aim-2 was designed to predict tamoxifen treatment efficacy based on MD reduction and the CYP2D6 genotyping that are known to affect the metabolism of tamoxifen to active compounds that have a high affinity for estrogen receptors. Patients returning to hospital for in-person follow-up will be invited to provide blood samples for the CYP2D6 genotyping, by using a new method based on the high-resolution melting curve analysis (HRM), which has been validated in Chinese women and proven to be efficient and low-cost. Based on the gene alleles patients will be determined as extensive metabolizers or intermediate metabolizers. The CYP2D6 metabolic status will be correlated with MD reduction, and then both correlated with prognosis. Further, they will be combined to investigate whether these two factors can be added to improve the prediction of prognosis.